Call:
lm(formula = loss_pct ~ PM + ECO, data = sra.frc.mss.lss.df[-which(sra.frc.mss.lss.df$Probe ==
"BSpp_comp_2001_18-28"), ])
Residuals:
Min 1Q Median 3Q Max
-4.1953 -0.9261 -0.4445 0.7965 5.7767
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.8136 0.5726 8.406 1.06e-10 ***
PMBS -0.1405 0.6486 -0.217 0.829539
PMGR -2.3959 0.6382 -3.754 0.000507 ***
ECOwf -1.0117 0.6382 -1.585 0.120098
ECOrf -1.7850 0.6394 -2.792 0.007729 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.831 on 44 degrees of freedom
Multiple R-squared: 0.3641, Adjusted R-squared: 0.3063
F-statistic: 6.299 on 4 and 44 DF, p-value: 0.0004255
Interestingly, it appears that AN soils have proportionally LESS minC by mass than do GR or BS soils, significantly so at depth. Why would this be? Possibly because AN soils have higher losses (DOC) during fractionation?
calculating Δ14C from fraction modern
`summarise()` has grouped output by 'PMeco'. You can override using the `.groups` argument.
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Error in rbind(deparse.level, ...) :
numbers of columns of arguments do not match
converting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to characterconverting IDs from factor to character
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$emtrends
PMeco year.trend SE df lower.CL upper.CL
ANpp -6.795 2.07 9 -11.47 -2.12
ANrf -1.018 2.07 9 -5.70 3.66
ANwf -0.681 2.07 9 -5.36 4.00
BSpp -1.842 2.07 9 -6.52 2.84
BSrf -2.766 2.07 9 -7.44 1.91
BSwf -1.453 2.07 9 -6.13 3.23
GRpp -4.189 2.07 9 -8.87 0.49
GRrf -0.779 2.07 9 -5.46 3.90
GRwf -2.078 2.07 9 -6.76 2.60
Confidence level used: 0.95
$contrasts
contrast estimate SE df t.ratio p.value
ANpp - ANrf -5.7768 2.92 9 -1.975 0.5893
ANpp - ANwf -6.1137 2.92 9 -2.090 0.5287
ANpp - BSpp -4.9532 2.92 9 -1.694 0.7388
ANpp - BSrf -4.0290 2.92 9 -1.378 0.8815
ANpp - BSwf -5.3424 2.92 9 -1.827 0.6689
ANpp - GRpp -2.6063 2.92 9 -0.891 0.9879
ANpp - GRrf -6.0161 2.92 9 -2.057 0.5460
ANpp - GRwf -4.7170 2.92 9 -1.613 0.7792
ANrf - ANwf -0.3370 2.92 9 -0.115 1.0000
ANrf - BSpp 0.8235 2.92 9 0.282 1.0000
ANrf - BSrf 1.7477 2.92 9 0.598 0.9991
ANrf - BSwf 0.4344 2.92 9 0.149 1.0000
ANrf - GRpp 3.1704 2.92 9 1.084 0.9630
ANrf - GRrf -0.2394 2.92 9 -0.082 1.0000
ANrf - GRwf 1.0598 2.92 9 0.362 1.0000
ANwf - BSpp 1.1605 2.92 9 0.397 1.0000
ANwf - BSrf 2.0847 2.92 9 0.713 0.9971
ANwf - BSwf 0.7714 2.92 9 0.264 1.0000
ANwf - GRpp 3.5074 2.92 9 1.199 0.9377
ANwf - GRrf 0.0976 2.92 9 0.033 1.0000
ANwf - GRwf 1.3967 2.92 9 0.478 0.9998
BSpp - BSrf 0.9242 2.92 9 0.316 1.0000
BSpp - BSwf -0.3891 2.92 9 -0.133 1.0000
BSpp - GRpp 2.3469 2.92 9 0.802 0.9937
BSpp - GRrf -1.0629 2.92 9 -0.363 1.0000
BSpp - GRwf 0.2362 2.92 9 0.081 1.0000
BSrf - BSwf -1.3133 2.92 9 -0.449 0.9999
BSrf - GRpp 1.4227 2.92 9 0.486 0.9998
BSrf - GRrf -1.9871 2.92 9 -0.679 0.9979
BSrf - GRwf -0.6880 2.92 9 -0.235 1.0000
BSwf - GRpp 2.7360 2.92 9 0.935 0.9838
BSwf - GRrf -0.6737 2.92 9 -0.230 1.0000
BSwf - GRwf 0.6254 2.92 9 0.214 1.0000
GRpp - GRrf -3.4098 2.92 9 -1.166 0.9459
GRpp - GRwf -2.1107 2.92 9 -0.722 0.9968
GRrf - GRwf 1.2991 2.92 9 0.444 0.9999
P value adjustment: tukey method for comparing a family of 9 estimates
Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc ==
"fPOM" & dens.01.09.19.sp.df$lyr_bot == 10, ])
Residuals:
Min 1Q Median 3Q Max
-36.987 -8.459 -3.794 14.059 31.963
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 165.512 25.131 6.586 0.000101 ***
year_i -6.795 2.068 -3.286 0.009443 **
pmbasalt -43.636 35.541 -1.228 0.250674
pmgranite -23.223 35.541 -0.653 0.529818
ecocool -168.432 35.541 -4.739 0.001060 **
ecocold -104.629 35.541 -2.944 0.016382 *
year_i:pmbasalt 4.953 2.925 1.694 0.124591
year_i:pmgranite 2.606 2.925 0.891 0.396052
year_i:ecocool 6.114 2.925 2.090 0.066144 .
year_i:ecocold 5.777 2.925 1.975 0.079678 .
pmbasalt:ecocool 93.779 50.262 1.866 0.094932 .
pmgranite:ecocool 103.988 50.262 2.069 0.068483 .
pmbasalt:ecocold 62.329 50.262 1.240 0.246303
pmgranite:ecocold 17.972 50.262 0.358 0.728903
year_i:pmbasalt:ecocool -5.725 4.136 -1.384 0.199702
year_i:pmgranite:ecocool -4.003 4.136 -0.968 0.358422
year_i:pmbasalt:ecocold -6.701 4.136 -1.620 0.139664
year_i:pmgranite:ecocold -2.367 4.136 -0.572 0.581154
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 26.38 on 9 degrees of freedom
Multiple R-squared: 0.8821, Adjusted R-squared: 0.6595
F-statistic: 3.962 on 17 and 9 DF, p-value: 0.02039
Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc ==
"fPOM" & dens.01.09.19.sp.df$lyr_bot == 20, ])
Residuals:
Min 1Q Median 3Q Max
-26.206 -9.681 -4.089 6.602 47.172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.039 28.446 2.743 0.0227 *
year_i -4.217 2.341 -1.801 0.1052
pmbasalt 2.083 40.229 0.052 0.9598
pmgranite -34.956 40.229 -0.869 0.4075
ecocool -127.141 40.229 -3.160 0.0115 *
ecocold -68.688 40.229 -1.707 0.1219
year_i:pmbasalt 3.623 3.311 1.094 0.3022
year_i:pmgranite 8.217 3.311 2.482 0.0349 *
year_i:ecocool 4.120 3.311 1.245 0.2447
year_i:ecocold 3.572 3.311 1.079 0.3086
pmbasalt:ecocool 71.432 56.892 1.256 0.2409
pmgranite:ecocool 113.279 56.892 1.991 0.0777 .
pmbasalt:ecocold 47.147 56.892 0.829 0.4287
pmgranite:ecocold 37.800 56.892 0.664 0.5231
year_i:pmbasalt:ecocool -6.787 4.682 -1.450 0.1811
year_i:pmgranite:ecocool -10.673 4.682 -2.280 0.0486 *
year_i:pmbasalt:ecocold -5.429 4.682 -1.160 0.2761
year_i:pmgranite:ecocold -8.132 4.682 -1.737 0.1164
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 29.86 on 9 degrees of freedom
Multiple R-squared: 0.8625, Adjusted R-squared: 0.6027
F-statistic: 3.32 on 17 and 9 DF, p-value: 0.03583
Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc ==
"fPOM" & dens.01.09.19.sp.df$lyr_bot == 30, ])
Residuals:
Min 1Q Median 3Q Max
-32.57 -11.84 -3.65 7.70 58.63
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.7862 34.5553 -0.225 0.827
year_i -0.5033 2.8436 -0.177 0.863
pmbasalt 57.2270 48.8686 1.171 0.272
pmgranite 19.0016 48.8686 0.389 0.706
ecocool -67.2614 48.8686 -1.376 0.202
ecocold -27.5273 48.8686 -0.563 0.587
year_i:pmbasalt 0.5886 4.0215 0.146 0.887
year_i:pmgranite 3.3000 4.0215 0.821 0.433
year_i:ecocool 0.9728 4.0215 0.242 0.814
year_i:ecocold 1.3306 4.0215 0.331 0.748
pmbasalt:ecocool 45.7950 69.1106 0.663 0.524
pmgranite:ecocool 65.6280 69.1106 0.950 0.367
pmbasalt:ecocold -1.7179 69.1106 -0.025 0.981
pmgranite:ecocold 3.6995 69.1106 0.054 0.958
year_i:pmbasalt:ecocool -7.3011 5.6873 -1.284 0.231
year_i:pmgranite:ecocool -5.8728 5.6873 -1.033 0.329
year_i:pmbasalt:ecocold -2.4287 5.6873 -0.427 0.679
year_i:pmgranite:ecocold -3.7023 5.6873 -0.651 0.531
Residual standard error: 36.27 on 9 degrees of freedom
Multiple R-squared: 0.776, Adjusted R-squared: 0.353
F-statistic: 1.835 on 17 and 9 DF, p-value: 0.178
Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc ==
"minC" & dens.01.09.19.sp.df$lyr_bot == 10, ])
Residuals:
Min 1Q Median 3Q Max
-32.445 -14.690 -0.982 8.313 49.666
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.235715 31.836582 1.672 0.1288
year_i -1.368463 2.619903 -0.522 0.6141
pmbasalt -11.392863 45.023726 -0.253 0.8059
pmgranite 13.808110 45.023726 0.307 0.7661
ecocool -129.550490 45.023726 -2.877 0.0183 *
ecocold -53.252323 45.023726 -1.183 0.2672
year_i:pmbasalt 1.011690 3.705102 0.273 0.7910
year_i:pmgranite -0.008576 3.705102 -0.002 0.9982
year_i:ecocool 3.341545 3.705102 0.902 0.3906
year_i:ecocold -0.800799 3.705102 -0.216 0.8337
pmbasalt:ecocool 100.351702 63.673164 1.576 0.1495
pmgranite:ecocool 101.017634 63.673164 1.587 0.1471
pmbasalt:ecocold -2.086764 63.673164 -0.033 0.9746
pmgranite:ecocold -45.174314 63.673164 -0.709 0.4960
year_i:pmbasalt:ecocool -2.576587 5.239806 -0.492 0.6347
year_i:pmgranite:ecocool -3.747488 5.239806 -0.715 0.4926
year_i:pmbasalt:ecocold 2.650378 5.239806 0.506 0.6251
year_i:pmgranite:ecocold 4.370745 5.239806 0.834 0.4258
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 33.41 on 9 degrees of freedom
Multiple R-squared: 0.7665, Adjusted R-squared: 0.3254
F-statistic: 1.738 on 17 and 9 DF, p-value: 0.2008
Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc ==
"minC" & dens.01.09.19.sp.df$lyr_bot == 20, ])
Residuals:
Min 1Q Median 3Q Max
-25.918 -9.446 -2.474 9.149 31.546
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -9.5997 21.9478 -0.437 0.6721
year_i 0.7565 1.8061 0.419 0.6851
pmbasalt -8.2247 31.0389 -0.265 0.7970
pmgranite -11.4347 31.0389 -0.368 0.7211
ecocool -94.7296 31.0389 -3.052 0.0137 *
ecocold -15.3565 31.0389 -0.495 0.6326
year_i:pmbasalt 0.7698 2.5543 0.301 0.7700
year_i:pmgranite 3.0074 2.5543 1.177 0.2692
year_i:ecocool 1.2303 2.5543 0.482 0.6415
year_i:ecocold -2.6857 2.5543 -1.051 0.3205
pmbasalt:ecocool 127.3757 43.8956 2.902 0.0175 *
pmgranite:ecocool 124.8421 43.8956 2.844 0.0193 *
pmbasalt:ecocold -0.9729 43.8956 -0.022 0.9828
pmgranite:ecocold -31.2191 43.8956 -0.711 0.4950
year_i:pmbasalt:ecocool -4.6467 3.6123 -1.286 0.2304
year_i:pmgranite:ecocool -5.8824 3.6123 -1.628 0.1379
year_i:pmbasalt:ecocold 2.9547 3.6123 0.818 0.4345
year_i:pmgranite:ecocold 2.1864 3.6123 0.605 0.5599
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 23.04 on 9 degrees of freedom
Multiple R-squared: 0.8619, Adjusted R-squared: 0.6011
F-statistic: 3.305 on 17 and 9 DF, p-value: 0.03635
Call:
lm(formula = frc_14c ~ year_i * pm * eco, data = dens.01.09.19.sp.df[dens.01.09.19.sp.df$frc ==
"minC" & dens.01.09.19.sp.df$lyr_bot == 30, ])
Residuals:
Min 1Q Median 3Q Max
-21.580 -9.094 3.950 6.546 38.845
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -54.2864 20.6763 -2.626 0.0276 *
year_i 1.7628 1.7015 1.036 0.3272
pmbasalt -4.1368 29.2407 -0.141 0.8906
pmgranite -39.1905 29.2407 -1.340 0.2130
ecocool -58.1383 29.2407 -1.988 0.0780 .
ecocold 15.4194 29.2407 0.527 0.6107
year_i:pmbasalt -0.1398 2.4063 -0.058 0.9550
year_i:pmgranite 3.6500 2.4063 1.517 0.1636
year_i:ecocool -1.0213 2.4063 -0.424 0.6812
year_i:ecocold -5.3002 2.4063 -2.203 0.0551 .
pmbasalt:ecocool 132.3396 41.3525 3.200 0.0108 *
pmgranite:ecocool 133.4644 41.3525 3.227 0.0104 *
pmbasalt:ecocold -34.7870 41.3525 -0.841 0.4220
pmgranite:ecocold -6.5936 41.3525 -0.159 0.8768
year_i:pmbasalt:ecocool -4.8607 3.4030 -1.428 0.1870
year_i:pmgranite:ecocool -4.8928 3.4030 -1.438 0.1843
year_i:pmbasalt:ecocold 6.4371 3.4030 1.892 0.0911 .
year_i:pmgranite:ecocold 3.3074 3.4030 0.972 0.3565
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.7 on 9 degrees of freedom
Multiple R-squared: 0.8748, Adjusted R-squared: 0.6383
F-statistic: 3.699 on 17 and 9 DF, p-value: 0.0255
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
Call:
lm(formula = fPOM ~ d14c * pm * eco, data = dens.inc.reps.w)
Residuals:
Min 1Q Median 3Q Max
-59.479 -10.962 -2.964 13.622 56.625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -46.8010 16.9411 -2.763 0.00725 **
d14c 1.0008 0.2921 3.426 0.00101 **
pmbasalt 43.3810 24.1340 1.798 0.07639 .
pmgranite 43.0356 19.5950 2.196 0.03125 *
ecocool -38.8844 37.2750 -1.043 0.30031
ecowarm 15.4182 19.7989 0.779 0.43865
d14c:pmbasalt -0.4179 0.3512 -1.190 0.23791
d14c:pmgranite -0.2566 0.3554 -0.722 0.47254
d14c:ecocool -0.4688 0.5535 -0.847 0.39982
d14c:ecowarm 0.1097 0.3147 0.349 0.72834
pmbasalt:ecocool -14.2091 41.9264 -0.339 0.73565
pmgranite:ecocool 28.0420 39.6631 0.707 0.48181
pmbasalt:ecowarm 26.1699 29.2181 0.896 0.37337
pmgranite:ecowarm 15.1343 24.9114 0.608 0.54539
d14c:pmbasalt:ecocool 0.7267 0.5999 1.211 0.22967
d14c:pmgranite:ecocool 0.5756 0.5987 0.961 0.33951
d14c:pmbasalt:ecowarm -0.1299 0.4082 -0.318 0.75112
d14c:pmgranite:ecowarm -0.1381 0.3939 -0.350 0.72699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 22.62 on 73 degrees of freedom
Multiple R-squared: 0.8853, Adjusted R-squared: 0.8586
F-statistic: 33.14 on 17 and 73 DF, p-value: < 2.2e-16
Call:
lm(formula = minC ~ d14c * pm * eco + year, data = dens.inc.reps.w)
Residuals:
Min 1Q Median 3Q Max
-73.03 -10.91 2.62 13.50 57.87
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -121.9498 17.6236 -6.920 0.00000000155 ***
d14c 0.7669 0.2857 2.684 0.009017 **
pmbasalt 111.7400 23.6361 4.728 0.00001098055 ***
pmgranite 88.6981 19.0431 4.658 0.00001425529 ***
ecocool -8.2650 36.4407 -0.227 0.821217
ecowarm 82.1645 19.2602 4.266 0.00005960539 ***
year2019 20.4426 6.0751 3.365 0.001230 **
d14c:pmbasalt -0.8841 0.3444 -2.568 0.012318 *
d14c:pmgranite -0.5648 0.3455 -1.635 0.106480
d14c:ecocool -0.4567 0.5401 -0.846 0.400524
d14c:ecowarm -0.2215 0.3056 -0.725 0.470966
pmbasalt:ecocool -34.0345 41.5389 -0.819 0.415296
pmgranite:ecocool 17.2802 38.8323 0.445 0.657658
pmbasalt:ecowarm -131.4919 28.5973 -4.598 0.00001779658 ***
pmgranite:ecowarm -83.9280 24.2075 -3.467 0.000892 ***
d14c:pmbasalt:ecocool 1.1027 0.5893 1.871 0.065396 .
d14c:pmgranite:ecocool 0.6565 0.5863 1.120 0.266566
d14c:pmbasalt:ecowarm 1.2436 0.4000 3.109 0.002689 **
d14c:pmgranite:ecowarm 0.7410 0.3852 1.924 0.058362 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 21.96 on 72 degrees of freedom
Multiple R-squared: 0.8299, Adjusted R-squared: 0.7874
F-statistic: 19.52 on 18 and 72 DF, p-value: < 2.2e-16
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions
Currently analysing dataframe
Between 0 and 140 degrees C the proportion of sample C is 0.001488078
Between 140 and 245 degrees C the proportion of sample C is 0.03870194
Between 245 and 340 degrees C the proportion of sample C is 0.3571529
Between 340 and 390 degrees C the proportion of sample C is 0.2355014
Between 390 and 495 degrees C the proportion of sample C is 0.2936209
Between 495 and 791 degrees C the proportion of sample C is 0.07359725
Currently analysing dataframe
Between 0 and 141.5 degrees C the proportion of sample C is 0.002647777
Between 141.5 and 246.5 degrees C the proportion of sample C is 0.07616599
Between 246.5 and 290 degrees C the proportion of sample C is 0.1643875
Between 290 and 364 degrees C the proportion of sample C is 0.3792163
Between 364 and 484 degrees C the proportion of sample C is 0.3074933
Between 484 and 788 degrees C the proportion of sample C is 0.07006177
Currently analysing dataframe
Between 0 and 143 degrees C the proportion of sample C is 0.005167379
Between 143 and 248 degrees C the proportion of sample C is 0.07075111
Between 248 and 293 degrees C the proportion of sample C is 0.163502
Between 293 and 367 degrees C the proportion of sample C is 0.3797734
Between 367 and 487 degrees C the proportion of sample C is 0.3038745
Between 487 and 792 degrees C the proportion of sample C is 0.07718191